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to their cable news program. The New York Times does not know which parts of
the paper each subscriber reads. On the Web, though, cnn.com and nytimes.com
have a much better indication of readers™ interests. Connecting this source of
information back to individuals over time is challenging (not to mention the
challenge of connecting readers interests to advertising over time).
Customers are not all created equal. Nor should all customers be treated
equally, since some are clearly more valuable than others. Figure 14.1 shows a
continuum of customer relationships, from the perspective of the amount of
investment worthy of each relationship. Some customers merit very deep and
intimate relationships centered around people. Other customers are too
numerous and, individually, not valuable enough to maintain individual rela­
tionships. For this group, we need technology to help make the relationship
more intimate. The third group is perhaps the most challenging, because they
are in between those who merit real intimacy and those who merit feigned
intimacy. This group often includes small businesses as well as indirect rela­
tionships. The sidebar “No Customer Relationship” talks about another situa­
tion, companies that do not know about their end users and do not need to.
Data Mining throughout the Customer Life Cycle 449

Consumers Very small Small and medium Large businesses
(low intimacy) businesses businesses (deep intimacy)

Many customers Few customers
Each small contribution to profit Each large contribution to profit
Very important in aggregate Important individual and in aggregate
Technologies: Technologies:
Mass intimacy Sales force automation
Customer relationship management Account management support
Figure 14.1 Intimacy in customer relationships generally increases as the size of the
account increases.

Deep Intimacy
Customers who are worth a deep intimate relationship are usually large
organizations”business customers. These customers are big enough to devote
dedicated resources, in the form of account managers and account teams. The
relationship is usually some sort of business-to-business relationship. One-off
products and services characterize these relationships, making it difficult
to compare different customers, because each customer has a set of unique
An example is the branding triumvirate of McDonald™s, Coca-Cola, and
Disney. McDonald™s is the largest retailer of Coke products worldwide. When
Disney has special promotions in fast food restaurants for children™s movies,
McDonald™s gets first dibs at distributing the toys inside their Happy Meals.
And when Disney characters (at least the good guys!) drink soda or open the
refrigerator”Coke products are likely to be there. Coke also has exclusive
arrangements with Disney, so Disney serves Coke products at its theme parks,
in its hotels, and on its cruises. There are hundreds of people working together
to make this branding triumvirate work. Data mining, with even the most
advanced algorithms on even the fastest computers, is not going to replace
these people”nor will this process be automated in the conceivable future.
On the other hand, even large account teams and individual managers can
benefit from analysis, particularly around sales force automation tools. Data
mining analysis can help such groups work better, by providing an under­
standing of what is really going on. Data can still help find some useful
answers: which McDonald™s are particularly good at selling which soft drinks?
Where are product placements resulting in higher sales? What is the relation­
ship between weather and drink consumption at theme parks versus hotels?
And so on.
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The streets of Tokyo are lined with ubiquitous convenience stores that are
much like 7-11s or corner convenience stores in Manhattan. These stores carry
a small array of products, mostly food, including freshly made lunches. There
are three companies that dominate this market, Lawsons, Seven-Eleven Japan,
and Family Mart, the third largest of which processes about 20 million
transactions each day. Given that the population of Japan is a bit over 120
million, this means that, on average, every Japanese person purchases
something from one of these stores every other day. That is a phenomenal
amount of consumer interaction.
Dive a bit more deeply into the business. About the only thing these
companies know about their customers is that almost everyone who lives in
Japan is at least an occasional buyer. Transactions are almost exclusively cash-
based, so the companies have no way to tie a customer to a series of
transactions over time and in different stores.
The strength of these companies is really in distribution and payments. On
the distribution side, they are able to make three deliveries each day to the
stores, guaranteeing that lunchtime sushi is fresh and the produce hasn™t
wilted. Many people also use the stores near their homes to pay their bills with
cash, something that is very convenient in a cash-dominated society. Combining
these two businesses, some of the stores are becoming staging points for
orders, made through catalogs or over the Web. Customers can pay for and pick
up goods in their friendly, neighborhood convenience store.
Japanese convenience stores are an extreme example of businesses that
know very little about their end users. Packaged good manufacturers are
another example, because they do not own the retailing relationship.
Manufacturers only know when they have shipped goods to warehouses. End-
user information is still important, but the behavior is not sitting in their
databases, it is in the database of disparate retailers. To find out about
customer behavior, they might:
— Use industry-wide panels of customers to see how products are used
— Use surveys to find out about customers and when and how they use the
— Build relationships with retailers to get access to the point-of-sale data
— Listen to the data they are collecting, via complaints and compliments on
the Web, in call centers, and through the mail
Distribution data does still have tremendous value, giving an idea of what is
being sold when and where. Inside lurks information about which advertising
messages should go where and which products are more popular”and data
mining can be used for these things.
Data Mining throughout the Customer Life Cycle 451

On the business-to-business side, even large financial institutions can bene­
fit from understanding customers. One of the largest banks in the world
wanted to analyze foreign exchange transactions to determine which clients
would benefit from taking out a loan in one currency and repaying it in
another rather than taking out the loan in one currency and exchanging the
proceeds up front. The goal was to provide better products for the clients and
a longer-term relationship. However, people are then needed to interpret and
act on these results.
Although the deep relationship is often associated with large businesses,
this is not always the case. Private banking groups in retail banks work with
high net-worth individuals, and give them highly personalized service”
usually with a named banker managing their relationship. When a private
banking customer wants a loan or to make an investment, that person simply
calls his or her private banker. Private banking groups have traditionally been
highly profitable, so profitable that they can get away with almost anything.
The private banking group at one large bank was able to violate corporate
information technology standards, bringing in Macintosh computers and
AS400s, when the standards for the rest of the bank were Windows and Unix.
The private bank could get away with it; they were that profitable.
Also, just having large businesses as customers does not mean that each cus­
tomers necessarily merits such close attention. Directories, whether on the
Web or on yellow pages, have many business customers, but almost all are
treated equally. Although the customers include many large businesses, each
listing brings in a small amount of revenue so few are worth additional effort.

Mass Intimacy
At the other extreme is the mass intimacy relationship. Companies that are
serving a mass market typically have hundreds of thousands, or millions, or
tens of millions of customers. Although most customers would love to have
the attention of dedicated staff for all their needs, this is simply not economi­
cally feasible. Companies would have to employ armies of people to work
with customers, and the incremental benefit would not make up for the cost.
This is where data mining fits in particularly well with customer relation­
ship management. Many customer interactions are fully automated, especially
on the Web. This has the advantage of being highly scalable; however, it comes
at a loss of intelligence and warmth in the customer relationship. Using tech­
nology to make the relationship stronger is a multipronged effort:
Staff who work directly with customers (whether face-to-face, through

call centers, or via Web-enabled interfaces) must be trained to treat cus­
tomers respectfully, while at the same time trying to expand the rela­
tionship using enhanced information about customers.
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Automated systems need to be flexible, so different messages can be

directed to different customers. This clearly applies on the Web, but it
also applies to billing inserts, cashier receipts, background scripts read
while customers are on hold, and so on.
Both staff and automated systems that work with customers need to be

able to respond to new practices and new messages. Sometimes, these
new approaches come from the good ideas of staff. Sometimes, they
come from careful analysis and data mining. Sometimes, from a combi­
nation of the two.
This is an extension of the virtuous cycle of data mining. Learning”
whether accomplished through algorithms or through people”needs to be
acted upon. Rolling out results is as necessary as getting them in the first place.

Success involves working with call centers and training personnel who come

in contact with customers. Customer interactions over the Web have the
advantage that they are already automated, making it possible to complete the
virtuous cycle electronically. People are still involved in the process to manage
and validate the results. However, the Web makes it possible to obtain data,
analyze it, act on the results, and measure the effects without ever leaving the
electronic medium.

The goal of customer understanding can conflict with the goal of efficient
channel operation. One large mobile telephone company in the United States,
for instance, tried asking customers for their email addresses when they called
in with service related questions. Having the email address has many benefits.
For one thing, future service questions could be handled over the Web at a
lower cost than through the call center. It also opens the possibility for occa­
sional marketing messages, cross-sell, and retention opportunities. However,
because the questions added several seconds to the average call length, the call
center stopped asking. For the call center, getting on to the next call was more
important than enhancing the relationship with each customer.

WA R N I N G Privacy is a major concern, particularly for individual customers.
However, it is peripheral to data mining itself. To a large extent, the concern is
more about companies sharing data with each other rather than about a single
company using data mining on its own to understand customer behavior. In
some jurisdictions, it may be illegal to use information collected for operational
purposes for another purpose such as marketing or improving customer

Data Mining throughout the Customer Life Cycle 453

Mass intimacy also brings up the issue of privacy, which has become a major
concern with the growth of the Web. To the extent that we are studying cus­
tomer behavior, the data sources are the transactions between the customer
and the company”data that companies typically can use for business pur­
poses such as CRM (although there are some legal exceptions even to this). The
larger concern is when companies sell information about individuals.
Although such data may be useful when purchased, or may be a valuable
source of revenue, it is not a necessary part of data mining.

In-between Relationships
The in-between relationship is perhaps the most challenging. These are the
customers who are not big enough to warrant their own account teams, but are
big enough to require specialized products and services. These may be small
and medium-sized businesses. However, there are other groups, such as so-
called “mass affluent” banking customers, who do not have quite enough
assets to merit private banking yet who still do want special attention.
These customers often have a wider array of products, or at least of pricing
mechanisms”discounts for volume purchases, and so on”than mass inti­
macy customers. They also have more intense customer service demands, hav­
ing dedicated call centers and Web sites. There are often account specialists
who are responsible for dozens or hundreds of these relationships at the same
time. These specialists do not always give equal attention to all customers. One
use of data mining is in spreading best practices”finding what has been
working and has not been working and spreading this information.
When there are tens of thousands of customers, it is also possible to use data
mining directly to find patterns that distinguish good customers from bad,
and for determining the next product to sell to a particular customer. This use
is very similar to the mass intimacy case.

Indirect Relationships
Indirect relationships are another type of customer relationship, where inter­
mediate agents broker the relationship with end users. For instance, insurance
companies sell their products through agents, and it is often the agent that
builds the relationship with the customer. Some are captive agents that only
sell one company™s policies; others offer an assortment of products from dif­
ferent companies.
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Such agent relationships pose a business challenge. For instance, an insur­
ance company once approached Data Miners, Inc. to build a model to deter­
mine which policyholders were likely to cancel their policies. Before starting
the project, the company realized what would happen if such a model were
put in place. Armed with this information, agents would switch high-risk
policyholders to other carriers”accelerating the loss of these accounts rather
than preventing it. This company did not go ahead with the project. Perhaps
part of the problem was a lack of imagination in figuring out appropriate inter­


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